Tracking and Statistics Method Based on LBTM for Traffic Car Flow

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• 作者：Jian Liu (9)
  Zhiheng Gong (9)
  Xin Wang (9)
  Enyang Gao (9)
  Zexian Xu (10)
  
• 刊名：Lecture Notes in Control and Information Sciences
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：452
• 期：1
• 页码：277-292
• 全文大小：2309KB
• 作者单位：Jian Liu (9)
  Zhiheng Gong (9)
  Xin Wang (9)
  Enyang Gao (9)
  Zexian Xu (10)
  
  9. College of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, 110168, China
  10. School of Computing, National University of Singapore, Singapore, ?117417, Singapore
  

文摘

Since limitations have been encountered in the achievement of the traditional targets tracking algorithms, a novel tracking method based on local block-graphs targets matching is proposed. Car flow tracking is applied to prove the effectiveness of the method. First, the images containing the targets are captured by the video frames to get the targets block-graphs. Second, the block-graphs can be used to achieve targets matching, and the detection process can be achieved by targets matching to get the optimal targets set. Finally, the targets set can be used to achieve the minimum deviation forecasting of all frames, and the targets tracking can be achieved. A junction video is selected as the experimental data, and a large number of experiments have been done. The results show that the proposed method not only has a great effect to track cars, but also has a better detection rate and tracking accuracy. The car flow statistics can be completed effectively.